Enhancing irrigation management: Unsupervised machine learning coupled with geophysical and multispectral data for informed decision-making in rice production
Integrating diverse data sources for site-specific management zones (SSMZ) in precision agriculture is a complex task. Soil surveys using apparent electrical conductivity (ECa) have proven effective in capturing field variability. However, relying solely on one sensing data type may not fully ca...
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Artículo |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2025
|
| Materias: | |
| Acceso en línea: | https://www-sciencedirect-com.recursos.agrosavia.co/science/article/pii/S2772375524002405?via%3Dihub http://hdl.handle.net/20.500.12324/41165 https://doi.org/10.1016/j.atech.2024.100635 |
Ejemplares similares: Enhancing irrigation management: Unsupervised machine learning coupled with geophysical and multispectral data for informed decision-making in rice production
- Assessment of Spatial Variability of Soil Properties Using Different Methods to Identify Management Zones for Rotational Cropping Systems: A Case Study from Colombia
- Manual para el diagnóstico de cadmio en el cultivo de arroz (Oryza sativa L.) y su inmovilización mediante el uso de hongos formadores de micorrizas arbusculares
- Exploring non‑conventional irrigation methods and drone‑based monitoring to enhance water use efciency: a case of rice cultivation in Colombia
- Irrigation and nutrition as criteria for adequate management of Tahiti acid lime trees affected by a physiological disorder in tropical conditions
- Identifying Genes Associated with Abiotic Stress Tolerance Suitable for CRISPR/Cas9 Editing in Upland Rice Cultivars Adapted to Acid Soils
- An ecological, environmental, and economic indicators-based approach towards enhancing sustainability in water and nutrient use for passion fruit cultivation in Colombia